Technology for media playback is provided. In one example, a method may include identifying playback of media. Wakefulness data may be collected during the consumption from a wakefulness detector. A determination may be made whether an asleep state exists. The determination may be based on the wakefulness data collected from the wakefulness detector and based on historical user sleep data. The method may include terminating the playback of the media when the asleep state is determined to exist and resuming playback of the media upon request.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computing device that is configured to resume playback of digital media, comprising: a processor; a memory in electronic communication with the processor; instructions stored in the memory, the instructions being executable by the processor to: identify playback of the digital media for consumption; collect wakefulness data from a plurality of discrete devices, the wakefulness data being indicative of a wakefulness during the consumption of the digital media and at periodic intervals during the consumption; determine a state of reduced wakefulness at the periodic intervals by comparing the wakefulness data with historical user sleep data and by weighting wakefulness data from the plurality of discrete devices based on historical accuracy of the plurality of discrete devices for predicting the state of reduced wakefulness; wherein the state of reduced wakefulness comprises an asleep state and a state at which a user is falling asleep; and wherein the historical user sleep data comprises historical wakefulness data; reduce a volume of audio associated with the playback of the digital media during the state at which the user is falling asleep; terminate the playback of the digital media at a termination point when the asleep state is determined to exist; set a resume playback position for the digital media at a resume point in the digital media, wherein the resume point is prior to the termination point; and resume playback of the digital media at the resume playback position when consumption of the digital media resumes.
A computing device automatically pauses and resumes media playback based on the user's wakefulness. The device monitors the user's wakefulness using data from multiple sensors (e.g., camera, microphone) collected at regular intervals. It compares this real-time wakefulness data with the user's historical sleep patterns to determine if the user is falling asleep or is already asleep. The system weights sensor data based on the historical accuracy of each sensor. If the user is falling asleep, the audio volume is reduced. If the user is asleep, playback stops, and a resume point is set slightly before the termination point. Playback resumes from this resume point when the user requests it.
2. The computing device of claim 1 , wherein the digital media comprises video media.
The computing device that automatically pauses and resumes media playback based on user wakefulness as described above works specifically with video media. The device monitors wakefulness, reduces volume when falling asleep, pauses when asleep, sets a resume point before the pause, and resumes playback on request.
3. The computing device of claim 1 , wherein the digital media comprises an audio program.
The computing device that automatically pauses and resumes media playback based on user wakefulness as described above works specifically with audio programs. The device monitors wakefulness, reduces volume when falling asleep, pauses when asleep, sets a resume point before the pause, and resumes playback on request.
4. The computing device of claim 1 , further configured to track sleep cycles of a user detected by a wearable computing device for use as the historical user sleep data.
The computing device that automatically pauses and resumes media playback based on user wakefulness as described above uses sleep cycle data, tracked by a wearable device, as the historical sleep data for comparison with real-time wakefulness data. This wearable data enhances the accuracy of detecting reduced wakefulness and triggering the pause/resume functionality. The device monitors wakefulness, reduces volume when falling asleep, pauses when asleep, sets a resume point before the pause, and resumes playback on request.
5. The computing device of claim 1 , further configured to: prompt a user to indicate whether the resume playback position is satisfactory; receive input from the user regarding a satisfactory resume playback position when the user indicates that the resume playback position is unsatisfactory; and modify the historical user sleep data based on the input from the user.
The computing device that automatically pauses and resumes media playback based on user wakefulness as described above includes a user feedback mechanism. The system prompts the user to confirm if the automatically chosen resume point is appropriate. If the user indicates the resume point is not satisfactory, they can provide input to adjust it. This user input is then used to refine the user's historical sleep data, improving the accuracy of future wakefulness detection and resume point selection. The device monitors wakefulness, reduces volume when falling asleep, pauses when asleep, sets a resume point before the pause, and resumes playback on request.
6. A computer-implemented method, comprising: receiving an instruction to initiate playback of media for consumption using a media consumption device; collecting wakefulness data at periodic intervals during the consumption from a plurality of detection devices; determining, using a processor, a state of reduced wakefulness by comparing the wakefulness data with historical user sleep data and based on the wakefulness data collected from the plurality of detection devices by weighting wakefulness data from the plurality of detection devices based on historical accuracy of the plurality of detection devices for predicting the state of reduced wakefulness; wherein the state of reduced wakefulness comprises an asleep state and a state at which a user is falling asleep; and wherein the historical user sleep data comprises historical wakefulness data; reducing a volume of audio associated with the playback of the media during the state at which the user is falling asleep; terminating the playback of the media when the asleep state exists; setting a playback position of the media to a point in the media that is consumed prior to the identification of the asleep state; and resuming playback of the media upon request, from the playback position.
A computer-implemented method automatically pauses and resumes media playback based on the user's wakefulness. Upon initiating media playback, the system collects wakefulness data from multiple sensors at regular intervals. A processor compares this real-time data to the user's historical sleep patterns to determine a state of reduced wakefulness (falling asleep or asleep). Sensor data is weighted based on historical accuracy. If falling asleep, the audio volume decreases. When asleep, playback stops. A resume point is set slightly before the detected asleep state. Playback resumes from that point when requested.
7. The method of claim 6 , further comprising collecting the wakefulness data using the plurality of detection devices, the plurality of detection devices being selected from the group consisting of: a camera, a microphone, an accelerometer, a heart rate monitor, a proximity sensor, a gyroscope, and a thermometer.
The method of automatically pausing and resuming media playback based on user wakefulness as described above uses various detection devices, including a camera, microphone, accelerometer, heart rate monitor, proximity sensor, gyroscope, and thermometer, to collect the wakefulness data. The combination of these sensor inputs enhances the accuracy of detecting reduced wakefulness. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
8. The method of claim 6 , further comprising collecting the wakefulness data using the plurality of detection devices, the plurality of detection devices including at least one of a camera, a microphone, an accelerometer or a heart rate monitor.
The method of automatically pausing and resuming media playback based on user wakefulness as described above uses a camera, microphone, accelerometer, or heart rate monitor (or some combination of these) to collect wakefulness data. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
9. The method of claim 6 , further collecting the wakefulness data using a detection device separate from the media consumption device.
The method of automatically pausing and resuming media playback based on user wakefulness as described above uses a separate device to collect wakefulness data, distinct from the device playing the media. This allows for potentially more accurate or unobtrusive monitoring of the user's state. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
10. The method of claim 6 , further comprising collecting the wakefulness data using a detection device integrated with the media consumption device.
The method of automatically pausing and resuming media playback based on user wakefulness as described above incorporates the wakefulness detection device directly into the media playback device. This provides a self-contained system for monitoring and adjusting playback. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
11. The method of claim 6 , further comprising learning wakefulness indicators that the asleep state exists using historical sleep data or using input from a user.
The method of automatically pausing and resuming media playback based on user wakefulness as described above learns the indicators of when a user is falling asleep or asleep, using historical sleep data or direct input from the user. This allows the system to adapt to individual user patterns and improve the accuracy of wakefulness detection over time. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
12. The method of claim 6 , further comprising sending an asleep signal to one or more connected devices to indicate that the asleep state exists and instruct the connected devices to follow pre-defined sleep settings.
The method of automatically pausing and resuming media playback based on user wakefulness as described above sends a signal to other connected devices when an asleep state is detected. This signal instructs those devices to activate pre-defined sleep settings (e.g., dimming lights, adjusting thermostat). The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
13. The method of claim 6 , further comprising predicting a last viewed playback position based on the wakefulness data and resuming the playback of the media at the last viewed playback position.
The method of automatically pausing and resuming media playback based on user wakefulness as described above predicts the last viewed playback position based on the collected wakefulness data and resumes playback from that point. This ensures minimal loss of content when the user falls asleep. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
14. The method of claim 6 , further comprising predicting a last viewed playback position based on the wakefulness data and resuming the playback of the media at a predetermined time interval prior to the last viewed playback position.
The method of automatically pausing and resuming media playback based on user wakefulness as described above predicts the last viewed playback position based on wakefulness data, but then resumes playback at a predetermined time interval (e.g., 10 seconds) *before* that point. This provides a small buffer to ensure the user doesn't miss anything. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
15. The method of claim 6 , wherein the media is divided into a plurality of discrete segments, the method further comprising predicting a last viewed playback position based on the wakefulness data and resuming the playback of the media at a beginning of one of the discrete segments prior to the last viewed playback position.
The method of automatically pausing and resuming media playback based on user wakefulness, where the media is segmented, predicts the last viewed playback position based on wakefulness data and resumes playback from the beginning of the previous segment. This is helpful for content like podcasts or audiobooks. The method involves collecting wakefulness data at intervals, comparing it to historical sleep data (weighting sensor data based on accuracy), reducing volume when falling asleep, pausing when asleep, setting a resume point before the pause, and resuming playback on request.
16. A non-transitory computer-readable medium comprising computer-executable instructions which, when executed by a processor, implement a system, comprising: a sleep data memory configured to store historical sleep data of a user; a wakefulness analysis module configured to detect wakefulness data of the user based on data collected from a plurality of discrete devices at periodic intervals, determine a wakefulness level by comparing the wakefulness data with historical user sleep data and by using the data from the plurality of discrete devices weighted based on historical accuracy of the plurality of discrete devices for predicting the wakefulness level, and wherein the wakefulness level comprises fully awake level, falling asleep level, and asleep level; and wherein the historical user sleep data comprises historical wakefulness data; set a playback position of the media to a point consumed prior to the identification of the asleep level; and a media server module configured to play the media via a playback device upon request from the user, to reduce a volume of audio associated with the playback of the media during the falling asleep level, to terminate the playback of the media when the wakefulness level of the user is below a predetermined threshold, and to resume playback of the media at the playback position upon request from the user.
A non-transitory computer-readable medium stores instructions for a system that manages media playback based on user wakefulness. The system includes: a memory that stores historical sleep data; a wakefulness analysis module that detects wakefulness from multiple sensors at intervals, determines wakefulness level by comparing data to historical sleep patterns (weighting sensor data by accuracy), and identifies fully awake, falling asleep, and asleep states; and a media server module that plays media, reduces volume when falling asleep, stops playback when asleep, sets the resume point before the detected asleep state, and resumes playback when requested.
17. The computer-readable medium of claim 16 , wherein the wakefulness analysis module is in electronic communication with the plurality of discrete devices configured to collectively detect a plurality of types of wakefulness indicators as the wakefulness data, the wakefulness analysis module being configured to determine the wakefulness level based on aggregated wakefulness indicators received from the wakefulness detection devices.
This invention relates to systems for monitoring and analyzing wakefulness levels using multiple discrete devices that collectively detect various types of wakefulness indicators. The system includes a wakefulness analysis module that receives wakefulness data from these devices, which are configured to detect different types of wakefulness indicators. The module processes and aggregates the indicators from the devices to determine an overall wakefulness level. The devices may include sensors or other detection mechanisms that measure physiological or behavioral signals associated with wakefulness, such as eye movement, muscle activity, or brainwave patterns. The aggregated data allows for a more comprehensive assessment of wakefulness compared to single-source measurements. The system is designed to improve accuracy in detecting wakefulness states, which can be applied in medical monitoring, driver alertness systems, or workplace safety applications. The wakefulness analysis module integrates and analyzes the diverse data streams to provide a unified wakefulness level, enhancing reliability and reducing false positives or negatives in wakefulness detection.
18. The computer-readable medium of claim 16 , wherein the media comprises video, audio or an electronic book.
The computer-readable medium for managing media playback based on user wakefulness as described above operates with video, audio, or electronic books. The system includes: a memory that stores historical sleep data; a wakefulness analysis module that detects wakefulness from multiple sensors at intervals, determines wakefulness level by comparing data to historical sleep patterns (weighting sensor data by accuracy), and identifies fully awake, falling asleep, and asleep states; and a media server module that plays media, reduces volume when falling asleep, stops playback when asleep, sets the resume point before the detected asleep state, and resumes playback when requested.
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March 11, 2015
May 30, 2017
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